{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ch06 字符串\n",
    "\n",
    "## 常用格式字符\n",
    "\n",
    "- [Python字符串方法汇总](https://www.w3school.com.cn/python/python_ref_string.asp)\n",
    "\n",
    "<style> \n",
    "table {float:left} \n",
    "</style>\n",
    "\n",
    "- `-:` 设置内容和标题栏居右对齐；\n",
    "\n",
    "- `:-` 设置内容和标题栏居左对齐；\n",
    "\n",
    "- `:-:` 设置内容和标题栏居中对齐。\n",
    "\n",
    "| 方法               | 描述                          |\n",
    "|:------------------|:------------------------------|\n",
    "| capitalize\\(\\)   | 把首字符转换为大写。                  |\n",
    "| casefold\\(\\)     | 把字符串转换为小写。                  |\n",
    "| center\\(\\)       | 返回居中的字符串。                   |\n",
    "| count\\(\\)        | 返回指定值在字符串中出现的次数。            |\n",
    "| encode\\(\\)       | 返回字符串的编码版本。                 |\n",
    "| endswith\\(\\)     | 如果字符串以指定值结尾，则返回 true。       |\n",
    "| expandtabs\\(\\)   | 设置字符串的 tab 尺寸。              |\n",
    "| find\\(\\)         | 在字符串中搜索指定的值并返回它被找到的位置。      |\n",
    "| format\\(\\)       | 格式化字符串中的指定值。                |\n",
    "| format\\_map\\(\\)  | 格式化字符串中的指定值。                |\n",
    "| index\\(\\)        | 在字符串中搜索指定的值并返回它被找到的位置。      |\n",
    "| isalnum\\(\\)      | 如果字符串中的所有字符都是字母数字，则返回 True。 |\n",
    "| isalpha\\(\\)      | 如果字符串中的所有字符都在字母表中，则返回 True。 |\n",
    "| isdecimal\\(\\)    | 如果字符串中的所有字符都是小数，则返回 True。   |\n",
    "| isdigit\\(\\)      | 如果字符串中的所有字符都是数字，则返回 True。   |\n",
    "| isidentifier\\(\\) | 如果字符串是标识符，则返回 True。         |\n",
    "| islower\\(\\)      | 如果字符串中的所有字符都是小写，则返回 True。   |\n",
    "| isnumeric\\(\\)    | 如果字符串中的所有字符都是数，则返回 True。    |\n",
    "| isprintable\\(\\)  | 如果字符串中的所有字符都是可打印的，则返回 True。 |\n",
    "| isspace\\(\\)      | 如果字符串中的所有字符都是空白字符，则返回 True。 |\n",
    "| istitle\\(\\)      | 如果字符串遵循标题规则，则返回 True。       |\n",
    "| isupper\\(\\)      | 如果字符串中的所有字符都是大写，则返回 True。   |\n",
    "| join\\(\\)         | 把可迭代对象的元素连接到字符串的末尾。         |\n",
    "| ljust\\(\\)        | 返回字符串的左对齐版本。                |\n",
    "| lower\\(\\)        | 把字符串转换为小写。                  |\n",
    "| lstrip\\(\\)       | 返回字符串的左修剪版本。                |\n",
    "| maketrans\\(\\)    | 返回在转换中使用的转换表。               |\n",
    "| partition\\(\\)    | 返回元组，其中的字符串被分为三部分。          |\n",
    "| replace\\(\\)      | 返回字符串，其中指定的值被替换为指定的值。       |\n",
    "| rfind\\(\\)        | 在字符串中搜索指定的值，并返回它被找到的最后位置。   |\n",
    "| rindex\\(\\)       | 在字符串中搜索指定的值，并返回它被找到的最后位置。   |\n",
    "| rjust\\(\\)        | 返回字符串的右对齐版本。                |\n",
    "| rpartition\\(\\)   | 返回元组，其中字符串分为三部分。            |\n",
    "| rsplit\\(\\)       | 在指定的分隔符处拆分字符串，并返回列表。        |\n",
    "| rstrip\\(\\)       | 返回字符串的右边修剪版本。               |\n",
    "| split\\(\\)        | 在指定的分隔符处拆分字符串，并返回列表。        |\n",
    "| splitlines\\(\\)   | 在换行符处拆分字符串并返回列表。            |\n",
    "| startswith\\(\\)   | 如果以指定值开头的字符串，则返回 true。      |\n",
    "| strip\\(\\)        | 返回字符串的剪裁版本。                 |\n",
    "| swapcase\\(\\)     | 切换大小写，小写成为大写，反之亦然。          |\n",
    "| title\\(\\)        | 把每个单词的首字符转换为大写。             |\n",
    "| translate\\(\\)    | 返回被转换的字符串。                  |\n",
    "| upper\\(\\)        | 把字符串转换为大写。                  |\n",
    "| zfill\\(\\)        | 在字符串的开头填充指定数量的 0 值。         |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[格式化字符串](https://www.cnblogs.com/wilber2013/p/4641616.html)\n",
    "- 在编写程序的过程中，经常需要进行格式化输出，每次用每次查。干脆就在这里整理一下，以便索引。\n",
    "- \"%\"是Python风格的字符串格式化操作符，非常类似C语言里的printf()函数的字符串格式化（C语言中也是使用%）。\n",
    "\n",
    "|格式化符号  |说明|\n",
    "|:----|:----|\n",
    "|%c     |转换成字符（ASCII 码值，或者长度为一的字符串|\n",
    "|%r        |优先用repr()函数进行字符串转换|\n",
    "|%s       |优先用str()函数进行字符串转换\n",
    "|%d / %i     |转成有符号十进制数|\n",
    "|%        |   转成无符号十进制数|\n",
    "|%o         |转成无符号八进制数|\n",
    "|%x / %X      |转成无符号十六进制数（x / X 代表转换后的十六进制字符的大小写）|\n",
    "%e / %E              |转成科学计数法（e / E控制输出e / E）|\n",
    "|%f / %              | 转成浮点数（小数部分自然截断）|\n",
    "| %g / %G              |%e和%f / %E和%F 的简写|\n",
    "|%%              |输出% （格式化字符串里面包括百分号，那么必须使用%%）|\n",
    "\n",
    "**这里列出的格式化符合都比较简单，唯一想要强调一下的就是\"%s\"和\"%r\"的差别**\n",
    "\n",
    "```python\n",
    "string = \"Hello\\tWill\\n\"\n",
    "print \"%s\" %string\n",
    "print \"%r\" %string\n",
    "```\n",
    "- **其实，这里的差异是str()和repr()两个内建函数之间的差异：**\n",
    "    - str()得到的字符串是面向用户的，具有较好的可读性\n",
    "    - repr()得到的字符串是面向机器的\n",
    "    - 通常（不是所有）repr()得到的效果是：obj == eval(repr(obj))\n",
    "\n",
    "### 格式化操作符辅助符\n",
    "| 辅助符号  | 说明|\n",
    "|:-----------|:---------------|\n",
    "| \\*    | 定义宽度或者小数点精度     |\n",
    "| \\-   | 用做左对齐  |\n",
    "| \\+  | 在正数前面显示加号\\(\\+\\)         |\n",
    "| \\# | 在八进制数前面显示零\\(0\\)，在十六进制前面显示\"0x\"或者\"0X\"（取决于用的是\"x\"还是\"X\"）|\n",
    "| 0     | 显示的数字前面填充\"0\"而不是默认的空格  |\n",
    "| \\(var\\) | 映射变量（通常用来处理字段类型的参数）     |\n",
    "| m\\.n| m 是显示的最小总宽度，n 是小数点后的位数（如果可用的话）     |\n",
    "\n",
    "```python\n",
    "num = 100\n",
    "\n",
    "\n",
    "print \"%d to hex is %X\" %(num, num)\n",
    "print \"%d to hex is %#x\" %(num, num)\n",
    "print \"%d to hex is %#X\" %(num, num) \n",
    "\n",
    "# 浮点数\n",
    "f = 3.1415926\n",
    "print \"value of f is: %.4f\" %f\n",
    "\n",
    "# 指定宽度和对齐\n",
    "students = [{\"name\":\"Wilber\", \"age\":27}, {\"name\":\"Will\", \"age\":28}, {\"name\":\"June\", \"age\":27}]\n",
    "print \"name: %10s, age: %10d\" %(students[0][\"name\"], students[0][\"age\"])\n",
    "print \"name: %-10s, age: %-10d\" %(students[1][\"name\"], students[1][\"age\"])\n",
    "print \"name: %*s, age: %0*d\" %(10, students[2][\"name\"], 10, students[2][\"age\"])\n",
    "\n",
    "# dict参数\n",
    "for student in students:\n",
    "print \"%(name)s is %(age)d years old\" %student\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = \"中国北京\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = \"BEIJING\"\n",
    "len(s1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 = \"中国北京beijing\"\n",
    "len(s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1834462752176"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testString ='good'\n",
    "id(testString)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'str' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-04601e18ea91>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtestString\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'b'\u001b[0m   \u001b[1;31m#Error not support item assignment\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'str' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "testString[0]='b'   #Error not support item assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1834462977136"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testString ='well'\n",
    "id(testString)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 to hex is 64\n",
      "100 to hex is 64\n",
      "100 to hex is 0x64\n",
      "100 to hex is 0X64\n"
     ]
    }
   ],
   "source": [
    "num = 100\n",
    "\n",
    "print (\"%d to hex is %x\" %(num, num))\n",
    "print( \"%d to hex is %X\" %(num, num))\n",
    "print (\"%d to hex is %#x\" %(num, num))\n",
    "print (\"%d to hex is %#X\" %(num, num) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value of f is: 3.1416\n"
     ]
    }
   ],
   "source": [
    "# 浮点数\n",
    "f = 3.1415926\n",
    "print (\"value of f is: %.4f\" %f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "students = [{\"name\":\"Wilber\", \"age\":27}, {\"name\":\"Will\", \"age\":28}, {\"name\":\"June\", \"age\":27}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name:     Wilber,\tage:         27\n",
      "name: Will      ,\tage: 28        \n",
      "name:       June, \tage: 0000000027\n"
     ]
    }
   ],
   "source": [
    "# 指定宽度和对齐\n",
    "print( \"name: %10s,\\tage: %10d\" %(students[0][\"name\"], students[0][\"age\"]))\n",
    "print (\"name: %-10s,\\tage: %-10d\" %(students[1][\"name\"], students[1][\"age\"]))\n",
    "print (\"name: %*s, \\tage: %0*d\" %(10, students[2][\"name\"], 10, students[2][\"age\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wilber is 27 years old\n",
      "Will is 28 years old\n",
      "June is 27 years old\n"
     ]
    }
   ],
   "source": [
    "# dict参数\n",
    "for student in students:\n",
    "    print( \"%(name)s is %(age)d years old\" %student)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## string包处理字符串"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hi, Wilber! Wilber is learning Python\n"
     ]
    }
   ],
   "source": [
    "from string import Template\n",
    "\n",
    "s = Template(\"Hi, $name! $name is learning $language\")\n",
    "print(s.substitute(name=\"Wilber\", language=\"Python\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hi, Will! Will is learning C#\n"
     ]
    }
   ],
   "source": [
    "d = {\"name\": \"Will\", \"language\": \"C#\"}\n",
    "print(s.substitute(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This book (TCP/IP) is 17$\n"
     ]
    }
   ],
   "source": [
    "# 用$$表示$符号\n",
    "s = Template(\"This book ($bname) is 17$$\")\n",
    "print(s.substitute(bname=\"TCP/IP\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__add__',\n",
       " '__class__',\n",
       " '__contains__',\n",
       " '__delattr__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__getnewargs__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__iter__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lt__',\n",
       " '__mod__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__rmod__',\n",
       " '__rmul__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " 'capitalize',\n",
       " 'casefold',\n",
       " 'center',\n",
       " 'count',\n",
       " 'encode',\n",
       " 'endswith',\n",
       " 'expandtabs',\n",
       " 'find',\n",
       " 'format',\n",
       " 'format_map',\n",
       " 'index',\n",
       " 'isalnum',\n",
       " 'isalpha',\n",
       " 'isascii',\n",
       " 'isdecimal',\n",
       " 'isdigit',\n",
       " 'isidentifier',\n",
       " 'islower',\n",
       " 'isnumeric',\n",
       " 'isprintable',\n",
       " 'isspace',\n",
       " 'istitle',\n",
       " 'isupper',\n",
       " 'join',\n",
       " 'ljust',\n",
       " 'lower',\n",
       " 'lstrip',\n",
       " 'maketrans',\n",
       " 'partition',\n",
       " 'replace',\n",
       " 'rfind',\n",
       " 'rindex',\n",
       " 'rjust',\n",
       " 'rpartition',\n",
       " 'rsplit',\n",
       " 'rstrip',\n",
       " 'split',\n",
       " 'splitlines',\n",
       " 'startswith',\n",
       " 'strip',\n",
       " 'swapcase',\n",
       " 'title',\n",
       " 'translate',\n",
       " 'upper',\n",
       " 'zfill']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2323'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 1235\n",
    "so = \"%o\" % x #八进制\n",
    "so"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4d3'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh=\"%x\" % x  #十六进制\n",
    "sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.235000e+03'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "se=\"%e\" % x #科学记数法\n",
    "se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ord(\"3\") #将字符3转换为对应的ascill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chr(ord(\"3\")+1)  #ord字符转换为ASCILL数值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'65'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"%s\"% 65 #字符串"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'65533'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"%s\" % 65533 #将65533以字符串输出\n",
    "\n",
    "# \"%d\" % \"555\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用format方法进行格式化\n",
    "\n",
    "**在占位符内，您可以添加格式化类型以格式化结果**：\n",
    "\n",
    "\n",
    "|符号|含义|\n",
    "|-----|:-------------------------------------------|\n",
    "| :<  | 左对齐结果（在可用空间内）                             |\n",
    "| :>   | 右对齐结果（在可用空间内）                             |\n",
    "| :^   | 居中对齐结果（在可用空间内）                            |\n",
    "| :=   | 将标志放置在最左侧                                 |\n",
    "| :\\+  | 使用加号指示结果是正数还是负数                           |\n",
    "| :\\-  | 负号仅用于负值                                   |\n",
    "| :    | 使用空格在正数之前插入一个多余的空格（在负数之前使用减号）             |\n",
    "| :,   | 使用逗号作为千位分隔符                               |\n",
    "| :\\_  | 使用下划线作为千位分隔符                              |\n",
    "| :b   | 二进制格式                                     |\n",
    "| :c  | 将值转换为相应的 unicode 字符                       |\n",
    "| :d  | 十进制格式                                     |\n",
    "| :e   | 科学格式，带有小写字母 E                             |\n",
    "| :E  | 科学格式，带有大写字母 E                             |\n",
    "| :f   | 定点数字格式                                    |\n",
    "| :F   | 定点数字格式，以大写形式显示（将 inf 和 nan 显示为 INF 和 NAN） |\n",
    "| :g       | 通用格式                                      |\n",
    "| :G       | 通用格式（将大写 E 用作科学计数法）                       |\n",
    "| :o   | 八进制格式                                     |\n",
    "| :x   | 十六进制格式，小写                                 |\n",
    "| :X   | 十六进制格式，大写                                 |\n",
    "| :n       | 数字格式                                      |\n",
    "| :%   | 百分比格式                                     |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 占位符\n",
    "\n",
    "- 可以使用`命名索引 {price}`、`编号索引{0}`、甚至空的占位符` {}` 来标识占位符。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number 5,555 in hex is: 0x15b3, the number 55 in oct is 0o67\n"
     ]
    }
   ],
   "source": [
    "print(\"The number {0:,} in hex is: {0:#x}, the number {1} in oct is {1:#o}\".format(5555,55))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number 55 in hex is: 0x37, the number 5555 in oct is 0o12663\n"
     ]
    }
   ],
   "source": [
    "print(\"The number {1:,} in hex is: {1:#x}, the number {0} in oct is {0:#o}\".format(5555,55))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "my name is Alex, my age is 37, and my QQ is 12345\n"
     ]
    }
   ],
   "source": [
    "print(\"my name is {name}, my age is {age}, and my QQ is {qq}\".format(name = \"Alex\",age = 37,qq = \"12345\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 占位符，可以通过编号索引`{0[1]}`,`{1[2]}`来引用`format`后面的内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X:5;Y:13;Z:8\n"
     ]
    }
   ],
   "source": [
    "position = (5,8,13)\n",
    "print(\"X:{0[0]};Y:{0[2]};Z:{0[1]}\".format(position))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X:5;Y:3;Z:8\n"
     ]
    }
   ],
   "source": [
    "position = (5,8,13)\n",
    "other = [1,2,3]\n",
    "print(\"X:{0[0]};Y:{1[2]};Z:{0[1]}\".format(position,other))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模板字符串"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weather of 'Monday' is 'rain'\n",
      "Weather of 'Tuesday' is 'sunny'\n",
      "Weather of 'Wednesday' is 'sunny'\n",
      "Weather of 'Thursday' is 'rain'\n",
      "Weather of 'Friday' is 'Cloudy'\n"
     ]
    }
   ],
   "source": [
    "weather = [(\"Monday\",\"rain\"),(\"Tuesday\",\"sunny\"),(\"Wednesday\", \"sunny\"),(\"Thursday\",\"rain\"),(\"Friday\",\"Cloudy\")]\n",
    "formatter = \"Weather of '{0[0]}' is '{0[1]}'\".format\n",
    "for item in map(formatter,weather):\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `format`表格，实战演练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The value is 2020                ！'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value = 2020\n",
    "\"The value is {0:<20}!\".format(value) # 左对齐"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The value is                 2020！'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"The value is {0:>20}!\".format(value) # 右对齐"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The value is         2020        ！'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"The value is {0:^20}!\".format(value) # j居中对齐\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The value is -------------------5！'"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value2 = -5\n",
    "\"The value is {0:-=20}!\".format(value2) # 将标志\"-\"放置在最左侧"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The value is -               5666！'"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value2 = -5666\n",
    "\"The value is {0:=20}!\".format(value2) # 将标志放置在最左侧,默认为空格\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The value is -56666!'"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value2 = -56666\n",
    "\"The value is {0: }!\".format(value2) # 用空格在正数之前插入一个多余的空格（在负数之前使用减号）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The value is 56666！'"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value2 = 56666\n",
    "\"The value is {:}!\".format(value2) # 使用空格在正数之前插入一个多余的空格（在负数之前使用减号）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The value is 56,666！'"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value2 = 56666\n",
    "\"The value is {:,}!\".format(value2) # 使用逗号分隔千分位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The value is 56_666！'"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value2 = 56666\n",
    "\"The value is {:_}!\".format(value2) # 使用下划线分隔千分位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The value is 56.666000%！'"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value2 = 0.56666\n",
    "\"The value is {:%}!\".format(value2) # 使用百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The value is -566666!'"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value2 = -566666\n",
    "\"The value is {:n}!\".format(value2) # 数字格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The value is 161f!'"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value2 = 5663\n",
    "\"The value is {0:x}!\".format(value2) # 使用百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The value is 161F!'"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value2 = 5663\n",
    "\"The value is {0:X}!\".format(value2) # 使用百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The value is                56.66!'"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value2 = 56.66\n",
    "\"The value is {0:>20}!\".format(value2) # 使用百分比"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 字符串常用方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- find 从头开始查找，并返回下标位置\n",
    "\n",
    "- `string.find(value, start, end)`\n",
    "\n",
    "- **定义和用法**\n",
    "- find() 方法查找指定值的首次出现。\n",
    "\n",
    "- 如果找不到该值，则 find() 方法返回 -1。\n",
    "\n",
    "- find() 方法与 index() 方法几乎相同，唯一的区别是，如果找不到该值，index() 方法将引发异常。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `isidentifier()`\n",
    "- **定义和用法**\n",
    "- 如果字符串是有效标识符，则` isidentifier()` 方法返回 True，否则返回 False。\n",
    "\n",
    "- 如果字符串仅包含字母数字字母（a-z）和（0-9）或下划线（_），则该字符串被视为有效标识符。有效的标识符不能以数字开头或包含任何空格。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n",
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "a = \"MyFolder\"\n",
    "b = \"Demo002\"\n",
    "c = \"2bring\"\n",
    "d = \"my demo\"\n",
    "\n",
    "print(a.isidentifier())\n",
    "print(b.isidentifier())\n",
    "print(c.isidentifier())\n",
    "print(d.isidentifier())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=\"apple,peach,banana,peach,pear\"\n",
    "s.find(\"peach\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index(\"peach\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.find(\"peach\",7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.find(\"peach\",7,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(s))\n",
    "s.rfind('p')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  `index` 和 `find`的区别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index('pear')\n",
    "# s.index('ppp')#抛出异常\n",
    "s.find(\"ppp\")#返回-1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### count 计算指定字符的个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=\"apple,peach,banana,peach,pear\"\n",
    "s.count('p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.count('pp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.count('ppp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## split\n",
    "\n",
    "- split() 方法将字符串拆分为列表。\n",
    "- 您可以指定分隔符，**默认分隔符是任何空白字符**。\n",
    "\n",
    "- 注释：若指定 max，列表将包含指定数量加一的元素。\n",
    "\n",
    "\n",
    "`string.split(separator, max)`\n",
    "\n",
    "|参数|\t描述|\n",
    "|:---|:----|\n",
    "|separator|\t可选。规定分割字符串时要使用的分隔符。默认值为空白字符。|\n",
    "|max|\t可选。规定要执行的拆分数。默认值为 -1，即“所有出现次数”。|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple', 'peach', 'banana', 'pear']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=\"apple,peach,banana,pear\"\n",
    "li=s.split(\",\")\n",
    "li"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hello', 'world', 'My', 'name', 'is', 'Alex']"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 'hello world \\n\\n My name is Alex   '\n",
    "s.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hello', 'world \\n\\n\\n My name is Alex   ']"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = '\\n\\nhello world \\n\\n\\n My name is Alex   '\n",
    "s.split(None,1) #只找第一个"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['\\n\\nhello\\t\\t world \\n\\n\\n My name\\t is', 'Alex']"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = '\\n\\nhello\\t\\t world \\n\\n\\n My name\\t is Alex   '\n",
    "s.rsplit(None,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hello', 'world', 'My name is Alex   ']"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = '\\n\\nhello\\t\\t world \\n\\n\\n My name is Alex   '\n",
    "s.split(None,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['\\n\\nhello\\t\\t world \\n\\n\\n My name', 'is', 'Alex']"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.rsplit(None,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hello', 'world', 'My', 'name', 'is', 'Alex   ']"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = '\\n\\nhello\\t\\t world \\n\\n\\n My name is Alex   '\n",
    "s.split(None,5)  #注意结尾的空格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hello', 'world', 'My', 'name', 'is', 'Alex']"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.split(None,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### partition定义和用法\n",
    "- partition() 方法搜索指定的字符串，并将该字符串拆分为包含三个元素的元组。\n",
    "\n",
    "- 第一个元素包含指定字符串之前的部分。\n",
    "\n",
    "- 第二个元素包含指定的字符串。\n",
    "\n",
    "- 第三个元素包含字符串后面的部分。\n",
    "- \n",
    "注释：此方法搜索指定字符串的第一个匹配项"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('apple', ',', 'peach,banana,pear')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.partition(',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### rpartition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'apple,peach,banana,peach,pear'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('apple,peach,banana', ',', 'pear')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.rpartition(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('apple,peach,', 'banana', ',peach,pear')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.partition(\"banana\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('apple,peach,', 'banana', ',pear')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.rpartition('banana')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2019', '10', '31']\n"
     ]
    }
   ],
   "source": [
    "s = \"2019-10-31\"\n",
    "t=s.split(\"-\")\n",
    "print (t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<map object at 0x000001AB209490D0>\n"
     ]
    }
   ],
   "source": [
    "print (map(int, t))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 字符串联接`join()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'apple,peach,banana,pear'"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "li=[\"apple\", \"peach\", \"banana\", \"pear\"]\n",
    "sep=\",\"\n",
    "s=sep.join(li)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'apple peach banana pear'"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "li=[\"apple\", \"peach\", \"banana\", \"pear\"]\n",
    "s = \" \".join(li)\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 其他常见函数\n",
    "- `lower()`、\n",
    "- `upper()`\n",
    "- `capitalize()`\n",
    "- `title()` 把每个单词的首字符转换为大写。\n",
    "- `swapcase()` 大小写反转，大写变小写，小写变大写"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'what is your name?'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = \"What is Your Name?\"\n",
    "s2 = s.lower()\n",
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'WHAT IS YOUR NAME?'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'What is your name?'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2.capitalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'What Is Your Name?'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'wHAT IS yOUR nAME?'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.swapcase()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#成员判断\n",
    "\"a\" in \"abcde\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"j\" in \"abcde\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0_Label_1.jpg', '0_Label_0.jpg']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#判断字符串是否以指定字符串开始或结束\n",
    "import os\n",
    "[filename for filename in os.listdir(r'/home/lab466/pythons/py35LessonPlusSpiderML/ipynb/data') if filename.endswith(('.bmp','.jpg','.gif'))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 字符串常量"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Python is u gryuty progrumming lunguugy. I liky it!'"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import string\n",
    "table=''.maketrans(\"abcdef123\",\"uvwxyz@#$\") # maketrans: 对应关系替换\n",
    "s=\"Python is a greate programming language. I like it!\"\n",
    "s.translate(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0123456789'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import string\n",
    "string.digits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `strip()`函数\n",
    "- strip() 方法删除任何前导（开头的空格）和尾随（结尾的空格）字符（空格是要删除的默认前导字符）。\n",
    "- **空白字符**——对于 `split()`和`rsplt()`方法：如果不指定分隔符：则字符中的空白符号（空格、换行符、制表符）等都将被认为是分隔符，返包含最终分割结果的列表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abc'"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=\" abc  \"\n",
    "s2=s.strip( )\n",
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello world'"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = '\\n\\nhello world\\n\\n'\n",
    "s2=s.strip( )\n",
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ssddf'"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"aaaassddf\".strip(\"a\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ssdd'"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"aaaassddf\".strip(\"af\") #删除a或者f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'aaaassddf'"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"aaaassddfaaa\".rstrip(\"a\") #只删除右边的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ssddfaaa'"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"aaaassddfaaa\".lstrip(\"a\") #只删除左边的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string.punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~ \\t\\n\\r\\x0b\\x0c'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#string.letters\n",
    "string.printable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abcdefghijklmnopqrstuvwxyz'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#string.lowercase\n",
    "string.ascii_lowercase  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ABCDEFGHIJKLMNOPQRSTUVWXYZ'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#string.uppercase\n",
    "string.ascii_uppercase "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `eval`函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval(\"3+4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 3\n",
    "b = 5\n",
    "eval('a+b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function sqrt in module math:\n",
      "\n",
      "sqrt(x, /)\n",
      "    Return the square root of x.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "eval('help(math.sqrt)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7320508075688772"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval('math.sqrt(3)')\n",
    "#eval('aa')    #Error 'aa' is not defined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please input a value:10+10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = input(\"Please input a value:\")\n",
    "#input value:  1+2\n",
    "eval(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    \n",
    "    #or input value: \"__import__('os').startfile(r'C:\\Windows\\\\notepad.exe')\"\n",
    "    eval(\"__import__('os').system('md testtest')\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `in`成员判断运算符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"a\" in \"abcde\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"j\" in \"abcde\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s='Beauty and Beast'\n",
    "s.startswith('Be')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.startswith('Be',5) #从5开始"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.startswith('Be',0,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bert801.csv', 'FR04014.csv', 'London_Westminster.csv', 'train.csv']"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "[filename for filename in os.listdir(r'./mynotebook/data/') if filename.endswith(('.bmp','.jpg','.gif','.txt',\"csv\"))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'1234abcd'.isalnum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'1234abcd'.isdigit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"1234\".isdigit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Abcd\".isalpha()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'1234abcd'.isalpha()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'    Hello world!    '"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Hello world!'.center(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'====Hello world!===='"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Hello world!'.center(20,'=')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello world!========'"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Hello world!'.ljust(20,'=')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'========Hello world!'"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Hello world!'.rjust(20,'=')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 下面部分在3.5中与2.7不同\n",
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0123456789'"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string.digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string.punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string.ascii_letters   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~ \\t\\n\\r\\x0b\\x0c'"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string.printable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abcdefghijklmnopqrstuvwxyz'"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string.ascii_lowercase  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ABCDEFGHIJKLMNOPQRSTUVWXYZ'"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string.ascii_uppercase "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\xe4\\xb8\\xad\\xe6\\x96\\x87'"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"中文\".encode(\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "UnicodeEncodeError",
     "evalue": "'ascii' codec can't encode characters in position 0-1: ordinal not in range(128)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnicodeEncodeError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-209-a09a7cf29060>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;34m\"中文\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"ascii\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mUnicodeEncodeError\u001b[0m: 'ascii' codec can't encode characters in position 0-1: ordinal not in range(128)"
     ]
    }
   ],
   "source": [
    "\"中文\".encode(\"ascii\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'ABC'"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"ABC\".encode(\"ascii\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'ABC'"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"ABC\".encode(\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ABC'"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b\"ABC\".decode(\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'中文'"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b'\\xe4\\xb8\\xad\\xe6\\x96\\x87'.decode(\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'中'"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b'\\xe4\\xb8\\xad\\xe6'.decode(\"utf-8\",errors=\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(b'\\xe4\\xb8\\xad\\xe6\\x96\\x87')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(\"中文\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(\"abc\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
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